This virtual session aims to promote the discussion of the opportunities, challenges, and visions of utilizing urban-geo big data to foster the understanding of human dynamics in transportation research. We welcome contributions on the following topics, but are not limited to:
• Methodology towards integrating urban-geo big data in transportation research.
• Application of integrating emerging urban-geo data (e.g., social media, open-source trajectory and imagery data, smartphone data, CAVs) in traffic assessment (safety and congestion), planning, and management.
• Smart mobility and smart city platforms, as well as physical systems for optimizing traffic controls.
• Application of new geo-visualization methods for exploring transportation big data.
• Application of Geo-AI in traffic dynamics modeling and simulation.
• Urban-geo big data quality assessment and enhancement.
• Strategies for encouraging data sharing and protecting data privacy & security.
• Multi-source data fusion challenges and considerations in transportation research.
Transportation research is undergoing a paradigm shift due to the emergence of big data. With the advancement of data acquisition and transmission techniques, unprecedented amounts of transportation data are continually being generated and collected from various data sources, such as social media, street-view imageries, road-side sensors, cellular signaling data, GPS-enabled smartphones, and connected & autonomous vehicles (CAVs). Compared to conventional data sources, these emerging urban-geo datasets are massive in size, spatiotemporally fine-scaled, and high dimensional (e.g., multivariate and multivalued), providing researchers with a rich source of information to gain new insights into transportation safety, congestion, planning, management, among others. However, managing and analyzing these big complex datasets expose new problems and challenges in terms of strategies to promote multiple aspects, including (1) data integration, enrichment, storage, archiving, and sharing, (2) data quality control (e.g., reduce data uncertainty and redundancy), (3) data security, integrity and privacy, and (4) data processing and visualization.
Dr. Xiao Li, Texas A&M Transportation Institute
Dr. Haowen Xu, Oak Ridge National Laboratory
Dr. Xiao Huang, University of Arkansas
Yuhao Kang, University of Wisconsin-Madison
|Presenter||Xiao Huang*, University of Arkansas, covid-19 exposes the long-standing social inequity issues in the U.S.||15||8:00 AM|
|Presenter||Zhiyuan Yao*, University of California, Los Angeles, Changjoo Kim, University of Cincinnati, Do people working at suburb centers experience less traffic congestion? An empirical study of individual trips in Cincinnati Metropolitan area||15||8:15 AM|
|Presenter||Jilin Hu*, , Mogahid Adam Hussein, Graduate Student, Yihong Yuan, Associate Professor, Khan Mortuza Bin Asad, Graduate Student, Extracting Dynamic Urban Mobility Patterns during COVID-19 from Bluetooth Tracking Data||15||8:30 AM|
|Presenter||Chenxiao (Atlas) Guo*, University of Wisconsin, Qunying Huang, University of Wisconsin-Madison, Song Gao, University of Wisconsin-Madison, Investigating Human Mobility During Pandemic: An Integrated Social Media Approach||15||8:45 AM|
|Presenter||Khan Mortuza Bin Asad*, Graduate Student, Yihong Yuan, Associate Professor, Modeling Human Mobility Using Texture Analysis||15||9:00 AM|
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